- Introduction
- This module provides an overview of the course and its objectives.
- Understanding the ML Enterprise Workflow
- This module discusses the ML enterprise workflow and the purpose of each step.
- Data in the Enterprise
- This module reviews Google’s enterprise data management and governance tools: Feature Store, Data Catalog, Dataplex, and Analytics Hub.
- Science of Machine Learning and Custom Training
- This module reviews the art and science of machine learning and neural networks. We'll also discuss how to train custom ML models using Vertex AI.
- Vertex Vizier Hyperparameter Tuning
- In this module we discuss how to do hyperparameter tuning using Vertex AI Vizier.
- Prediction and Model Monitoring Using Vertex AI
- This module covers Vertex AI prediction and model monitoring. We'll first discuss batch and online predictions using pre-built and custom containers, then we'll review model monitoring, which is a service that helps manage the performance of your ML models.
- Vertex AI Pipelines
- This module discusses Vertex AI pipelines and how to build them to orchestrate your ML workflow.
- Best Practices for ML Development
- This module reviews best practices for a number of different machine learning processes in Vertex AI.
- Course Summary
- This module is a summary of the Machine Learning in the Enterprise course.
- Series Summary
- This module is a summary of the Machine Learning on Google Cloud course series.